Matrices
The core of linear algebra, matrices are more than just grids of numbers. They represent systems, transformations, and more.
Matrix Properties
Delving deeper into matrix properties such as rank, determinant, and eigenvalues.
Linear Transformations
Go beyond mere definitions and visualize how matrices can represent various transformations.
Matrix as Transformations
See how every matrix corresponds to a unique transformation in space, including rotations, scaling, and projections.
Numpy: Advanced Operations
Numpy isn’t just for basic array operations. It’s a powerhouse for advanced mathematical computations.
Inner and Outer Products
Understanding the difference and significance of these two fundamental operations in vector spaces.
Multidimensional Arrays and Tensors
Dive into the world of higher-dimensional data structures. From 3D arrays to tensors, understand their significance in data representation.
Dimensions.. Dimensions..
The word “dimension” carries different meanings in mathematics, from spaces to the complexity of datasets.
From Vector Spaces to Hyperspaces
As we deal with multidimensional data, especially in fields like machine learning, the concept of “dimension” takes on new layers of meaning.
Parallel Computation Principles
In modern computation, parallelism is key. Explore the principles behind it and how it can be applied in matrix operations and other mathematical computations.
Advantages and Implementation
Why parallel computation? Discover its benefits and how it can be harnessed, especially in the context of large-scale matrix operations.
Dot Product & Cross Product Revised
Revisiting these foundational vector operations, this time with a more profound and nuanced understanding.
Mathematical and Geometric Significance
How do these operations translate in a geometric context? And how are they employed in various mathematical and practical scenarios?
